How I Used Python to Analyze S&P 500 Returns Since 1928

How I Used Python to Analyze S&P 500 Returns Since 1928 I have always heard financial advice like "the market averages 10% per year" and "time in the market beats timing the market." But I wanted to verify these claims with actual data. So I pulled 96 years of S&P 500 data and analyzed it with Python. Here is what I found — and some of the results challenge common assumptions. Getting the Data I used the historical S&P 500 annual returns dataset from NYU Stern professor Aswath Damodaran, which covers 1928-2024. You can replicate this with Yahoo Finance data using yfinance or download Damodaran's dataset directly. import pandas as pd import numpy as np # Load annual returns (1928-2024) # Columns: Year, S&P 500 Return (including dividends) df = pd.read_csv('sp500_returns.csv') print(f"Years of data: {len(df)}") print(f"Date range: {df['Year'].min()} - {df['Year'].max()}") Finding 1: The "Average 10%" Is Misleading The arithmetic mean annual return of the S&P 500 since